Anti-ship missile formation recognition target selection system based on Hough transformation and optimized K-means clustering

A technology of target selection and missile formation, applied in the field of formation identification, can solve the problems of indistinct distinction between key clustering segments, reduced method applicability, and complex clustering process, so as to improve the efficiency of iterative optimization and enhance engineering operations Sex, good recognition effect

Active Publication Date: 2021-12-31
NAVAL UNIV OF ENG PLA
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Problems solved by technology

It is known that the requirements for formation sorting are too high, which reduces the applicability of the method, and the judgment degree of different formations is not high, and the alignment angle has too much influence on the judgment; 3) Establish a formation template with the number of queue lines and queue angles, The mathematical model of the reference target, other targets relative to the queue orientation and similarity measure is given to judge whether the target is in the same queue line, and the number of queue lines is obtained by clustering to identify the formation. The similarity measure requires that the azimuth angle deviation must be smaller than the detection error , the judgment of the target being in the same queue line is not rigorous enough
[0004] Another type of formation recognition is to convert the target from the measurement space to the parameter space parameters, combined with formation templates for matching judgment, such as establishing a feature model and feature template for the formation shape and the distribution of group members in the formation, based on the template sliding Matching and identifying formation types, this method can identify linear formations, but the clustering process is complicated and the real-time performance is weak; at the same time, there is a method of K-means clustering algorithm that optimizes the number of clusters with the D / L cost function, D / L The cost function has a decreasing characteristic, the clustering number optimization and the clustering iterative process are independent of each other, there are multiple local optimal values, the key clustering numbers are not clearly differentiated, and the optimization number for typical clustering problems is slightly higher than the theoretical value
The common problem is that the impact of the detection interval on the clustering effect is not considered

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  • Anti-ship missile formation recognition target selection system based on Hough transformation and optimized K-means clustering
  • Anti-ship missile formation recognition target selection system based on Hough transformation and optimized K-means clustering
  • Anti-ship missile formation recognition target selection system based on Hough transformation and optimized K-means clustering

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Embodiment 1

[0110] see Figure 1 to Figure 35 , an anti-ship missile formation recognition target selection system based on Hough transform and optimized K-means clustering, including a movement situation monitoring module, a formation contour recognition module, a target situation establishment module, a strike target acquisition module, and a target strike module.

[0111] The movement situation monitoring module monitors the coordinates of all members in the target formation, thereby obtaining the movement situation of the target formation. The formation outline identification module performs Hough transformation on the movement situation of the target formation to identify the formation outline of the target formation. The target-pointing situation building module establishes a target-pointing situation model according to the formation outline of the target formation, and sorts and numbers all members of the target formation. The strike target acquisition module selects the strike ta...

Embodiment 2

[0204] The target selection method for anti-ship missile formation identification based on Hough transform and optimized K-means clustering mainly includes the following contents:

[0205] 1) Formation recognition target selection process for anti-ship missiles

[0206] To establish the formation matching recognition model of anti-ship missiles, the characteristics of the ship formation must be selected first. Although the number of ship formations varies under different circumstances, according to tactical purposes, common formation line formations include V-shaped formation, parallel formation, and circular air defense formation. Common formations are the most basic combinations of straight lines or circles, and they all have more typical geometric features. As long as the basic linear or circular shape can be recognized, the formation composition of the ship formation can be judged, and the formation matching recognition can be completed according to the relative positiona...

Embodiment 3

[0334] Situation scenario: In the combat sea area, our ship plans to use anti-ship missiles to attack the No. 3 ship in the enemy's V-shaped ship formation. Ignore the influence of sea conditions, weather, and network and space environment conditions on missiles; assume that the enemy ship formation has no countermeasures or evasion actions.

[0335] When the target indication is issued, the radar situation is as follows: Figure 12 shown. The formation elements of the enemy's V-shaped ship formation are set as follows: the reference ship J is the No. 5 ship, the formation heading γ=0°, and the horizontal distance of the formation is D h =0.5nmile, vertical spacing D z =1nmile, the left and right wings align angle α=26.6°. Before the missile is launched, the formation elements and preset targets are bound to the missile integrated control computer.

[0336] In order to verify the accuracy of the model in the noise state, the system noise and measurement noise interference ...

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Abstract

The invention discloses an anti-ship missile formation recognition target selection system based on Hough transformation and optimized K-means clustering. The anti-ship missile formation recognition target selection system comprises a motion situation monitoring module, a formation contour recognition module, a target finger situation establishment module, a striking target acquisition module and a target striking module. Based on the Hough transformation and the optimized K-means clustering algorithm, the anti-ship missile formation recognition target selection modeling method is provided, the efficiency is improved, the engineering operability is enhanced, and the method is of great significance to anti-ship combat simulation.

Description

technical field [0001] The invention relates to the field of formation recognition, in particular to an anti-ship missile formation recognition target selection system based on Hough transform and optimized K-means clustering. Background technique [0002] The target selection of anti-ship missiles usually has two types of technologies: feature recognition and formation recognition. Feature recognition relies on the single feature of the designated target that is different from other ships in the formation, or the comprehensive features weighted by multiple features. However, with the increase in the self-control time brought about by the increase in the range of anti-ship missiles, the changes in the situation of the terminal formation no longer meet the requirements determined before launch. characteristics, thereby exceeding the ability of anti-ship missile signature recognition. Formation recognition is to compare the formation situation captured by the missile terminal...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/02
CPCG05D1/0246G05D1/0257G05D1/0259G05D1/0221G05D1/0291G05D1/0276
Inventor 黄隽吴鹏飞刘方李晓宝张浩然刘玥
Owner NAVAL UNIV OF ENG PLA
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